Abstract #4772

Deep learning-based Lorentzian fitting of WASSR Z-spectra

Sajad Mohammed Ali¹, Nirbhay Yadav², Ronnie Wirestam¹, Munendra Singh³, Hye-Young Heo³, Peter van Zijl², and Linda Knutsson⁴

¹Medical Physics, Lund University, Lund, Sweden, ²Radiology, F.M. Kirby Research Center, Johns Hopkins University, Kennedy Krieger Institute, Baltimore, MD, United States, ³Radiology, Johns Hopkins University, Baltimore, MD, United States, ⁴F.M. Kirby Research Center, Radiology, Medical Radiation Physics, Kennedy Krieger Institute, Johns Hopkins University, Lund University, Baltimore, MD, United States

Synopsis

Keywords: Data Analysis, Machine Learning/Artificial Intelligence, Lorentzian curve fittingWater saturation shift referencing (WASSR) Z-spectra can be used to correct shifts due to B₀-field inhomogeneities, for magnetic susceptibility mapping and analysis of relaxation effects. The spectra follow a Lorentzian shape with discrete values. Hence, a Lorentzian fit to retrieve the shape parameters (amplitude A, line width LW and frequency shift Δf_H2O ) simplifies analysis. Conventionally, the least-squares (LS) method is used for such fitting despite being time consuming and sensitive to the unavoidable noise in vivo. We propose a deep learning-based Lorentzian-fitting neural network (LoFNet) that demonstrated improved robustness against noise and sampling density in combination with reduced time consumption.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.